1. Intra-Seasonal to Inter-Annual Predictabilty and Prediction (Acknowledgements) Deepthi AchuthavarierYoukyoung Jang Eric AltshulerJim Kinter Ben CashV. Krishnamurthy Tim DelSoleSanjiv Kumar Paul DirmeyerJulia Manganello Mike FennessyCristiana Stan Zhichang GuoDavid Straus Bohua HuangJieshun Zhu 1 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

2. Intra-Seasonal to Inter-Annual Predictabilty and Prediction Overarching Framework for Seasonal Predictability – COLA’s Role Role of Oceanic initial Conditions in ENSO Re-forecasts Seamless Prediction: The Role of Resolution Strategies for Doing Research with Flawed Parameterizations Predictability in a Changing Climate: Past, Present and Future 2 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

3. Overarching framework for Seasonal Predictability COLA’s Role “Predictability in the Midst of Chaos” Scientific Basis for Seasonal Predictability Slowly varying tropical SST and land surface act as forcing function for the seasonal mean circulation and intra-seasonal fluctuations (storm tracks, blocking, weather regimes) Thus: In coupled prediction, ocean and land initial conditions must be specified from observations/analyses! Need to know the sensitivities to uncertainties in the initial conditions of atmosphere, ocean and land (land not well studied) 3 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

4. 4 Bulletin of the Americal Meteorological Society Vol. 81, No. 11, November 2000 Spatial Variance of midlatitude geopotential due to tropical SST forcing: Probabilistic view from ensembles Compile a large number of samples of GCM integrations, where a sample is obtained by randomly drawing one ensemble member for each calendar win- ter. (Each sample is a series of seasonal means, comparable to observations.) JFM SST time series from Maximum Correlation Analysis (SVD) between tropical Pacific SST and 500 hPa mid-latitude geopotential fields in PNA region Geopotential height variance explained computed by regression onto SST time series Slowly varying tropical SST as forcing function DSP and PROVOST (European partner) DSP: Multi-agency, multi-model, multi-institution

5. COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research 5 Pacific North American Height variance explained by tropical SST (winter mean)

6. COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research 6 Circulation Regimes: Chaotic Variability versus SST-Forced PredictabilityDavid M. Straus, Susanna Corti, Franco MolteniJournal of ClimateVolume 20, Issue 10 (May 2007) pp. 2251-2272 Synoptic-Eddy Feedbacks and Circulation Regime AnalysisDavid M. StrausMonthly Weather ReviewVolume 138, Issue 11 (November 2010) pp. 4026-4034 Tropical SST Forcing, seasonal mean climate and low-frequency intraseasonal fluctuations Straus, D.M., S. Corti, and F. Molteni, 2007: J. Clim. 20, 2251-2272 Straus, D.M. 2010: Mon Wea. Rev. 138, 4026-4034 Frequency of occurrence depends on SST

7. Role of Oceanic Initial Conditions in ENSO Re-forecasts Model CFS version 2 provided by NCEP EMC Hindcast Experiments: 1) ATM/LND/ICE initial data from CFSRR 2) Four sets of forecasts differing in OCN initial data from ODA products: ECMWF COMBINE-NV, ECMWF ORA-S3, NCEP CFSR, NCEP GODAS 3) Anomaly Initialization for OCN initial state 4) 12-month hindcast starting 01 April for 1979-2007 (4 ensemble members) Validation Datasets: SST -- ERSST v3. Heat Content (HC) -- Ensemble Mean (EM) of six ODAs (above 4 ODAs + SODA + GFDL ECDA) 7 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

8. CFSv2 SST Predictive Skill (April ICs) Correlation for ICs from 4 ODAs 2-month forecast lead 5-month forecast lead 11-month forecast lead

9. Leading Months ( o C ) Prediction Skill of the Nino3.4 Index Combine-NV CFSRSuper_Ensemble

10. 10 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research Forecast Equatorial Heat Content Anomaly vs. OBS COMBINE-NV ORA-S3CFSRGODAS OBS

11. ENSO Forecast Summary ENSO prediction skill can depend significantly on the ODA used to initialize the ocean. The slightly worse performance of the prediction initializing from CFSR is attributed to its slight difference in the upper ocean heat content, possibly in the off-equatorial domain. 11 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

12. Are we still dependent upon and/or limited by parameterizations of convection and other processes? The Athena Project ECMWF Integrated Forecast System (IFS) - AGCM -13-month runs at a variety of horizontal resolutions: T159 (125 km), T511 (39 km), T1279 (16 km), T2047 (10 km) -AMIP runs (1961-2007) at a variety of horizontal resolutions -No re-tuning of convective parameterizations NICAM (almost no parameterizations) -Seasonal runs 12 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research Seamless Prediction: The Role of Resolution

13. COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research 13 Manganello, et al., 2011: Tropical Cyclone Climatology in a 10-km Global Atmospheric GCM: Toward Weather-Resolving Climate Modelling. Atlantic Tropical Cyclones Track genesis in left panels Track densities in right panels Higher resolution is necessary OBS T2047 T1279 T511 T156 GENESISDENSITY

14. COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research 14 black line: Observed green line – T159 (multiplied by 10) red line – T1279 (multiplied by 2) dashed line – Nino 3.4 (multiplied by -1) Power Dissipation Index North Atlantic (May-Nov 1975-2007) from AMIP and Obs

15. Indian Monsoon JJAS Precipitation IFS (reduced to N80) 1961-2008, T2047 1990-2008 TRMM 1998-2009 (mm/day) 15 TRMM T159 T511 T1279 T2047 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research Increased resolution only makes the systematic error worse !

16. Strategies for Doing Research with Flawed Parameterizations Replace them: “Super-parameterization SP-CCSM” - embed a two-dimensional slab of one- dimensional cloud-resolving models in CCSM3 T42 – these replace the conventional convection parameterizations (South American Monsoon) Supplement them: Idealized added heating put into CAM3 to circumvent model’s poor moist response to SST anomalies (ENSO / Indian Monsoon relationship) Remove them: Try to resolve everything explicitly – (NICAM) Stochastic Parameterizations – Augment existing parameterizations 16 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

17. Oscillatory Modes in South American Monsoon System SP-CCSM: CCSM with embedded cloud-resolving models Observation CCSM No intraseasonal oscillation Intra-Seasonal Oscillation (MJO) Inter-Seasonal Oscillation (NAO) Multi-Channel Singular Spectrum Analysis of OLR period ~ 60 dperiod ~ 120 d

18. Added Heating for 1997 Monsoon Inserting idealized additional heating into CAM3 - Proxy for SST-forcing of tropics during developing warm ENSO event in JJAS - Full set of model parameterizations are retained – model can have non- linear moist feedbacks - Use idealized vertical stucture, and a realistic horizontal structure No Indian Ocean HeatingIndian Ocean Heating Included

19. JJAS Mean 850 hPa Streamfunction Response   1997 Exp without IO  1997 Exp with IO ERA40 Note: With added IO heating the Monsoon response is closer to normal, as observed !

20. Predictability in a Changing Climate: Past, Present and Future Evolution of uncertainty (spread of pdf) from initial state  synoptic weather  intra-seasonal time scales in the fully coupled system. Questions: Does the evolution of uncertainty through atmosphere, land and ocean depend systematically on the climate: Recent past, present and future climates? What particular coupled pathways of uncertainty evolution are initiated by uncertainty in the initial land states? (Will our ability to forecast ISI time scales get better or worse in the future?) What 20 th Century ISI phenomena can we re-forecast with current coupled models? 20 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

21. Predictability in a Changing Climate Design Considerations: Predictability and prediction skill are both model-dependent: Use both CCSM4 (1 o x 1 o ) and CFSv2 Baseline runs from recent past, present and future climates needed. Methodologies for introducing both “small” and “large” uncertainties in land initial states are needed (unique aspect of this design) Predictability (“perfect model”) runs and predictions should be made for multiple starting times of year, with adequate ensemble size. 21 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

22. Predictability in a Changing Climate CCSM4 1 o x1 o Predictability Experiments: 50-year baseline run from pre-industrial 1850 forcing conditions and ICs 50-year baseline run from 2000 forcing and ICs 50-year baseline run from 2050 scenario forcing and ICs For each baseline run: Define four classes based on calendar date (01 Dec, 01 May, 01 Jun, 01 Jul) For each calendar date: Choose 15 key years from the appropriate baseline run, based on ENSO-criterion Each calendar date + key year define a start date from the baseline run. For each start date: Construct 14 “large” land surface perturbations (15 IC states altogether) Construct 14 “small” land surface perturbations (15 IC states altogether) For each IC state run the model for 90 days (12 months for 01 Dec, 01 Jun) 22 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

23. Predictability in a Changing Climate Small land surface perturbations 14 new land states must be defined for each start date from the baseline run. Subclass one: land states taken from 1,2,3, …,7 days previous to the start date Subclass two: land states taken from 0.5, 1.5, …., 6.5 days previous (defined by linear interpolation ) 23 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research Each horizontal black line represents a baseline run Each orange circle represents a key year

24. Predictability in a Changing Climate Large land surface perturbations 14 new land states must be defined for each start date from the baseline run. These land states are taken from the same calendar date but from the 14 other key years 24 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research Each horizontal black line represents a baseline run Each column of blue circles represents a key year

25. Evolution of small and large land errors (1850 baseline run) Soil Moisture Root Zone (all land) Shaded region are 95% uncertainty range for respective mean 25 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research Common atmosphere IC forces early convergence of pdf Large perturbations Small perturbations

26. Evolution of small and large land errors (2000 baseline run) Soil Moisture Root Zone (all land) Shaded region are 95% uncertainty range for the respective mean 26 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research Large perturbations Small perturbations

27. Signal/Total Initial land state has three regimes of impact on temperature predictability: 1.First two weeks: steady significant global impact. 2.Second two weeks: rapid decay of effects. 3.Beyond 30 days: limited to a few regions. CCSM-4 Days from May 1

28. Predictability from Coupling Top: CCSM4 (1850) correlation between initial ½ day soil moisture perturbations and 1-day T 2m anomalies. Bottom: GSWP2 seasonal index of coupling between soil moisture and evaporation. Red shading links high land IC impacts on atmosphere (top) to strong land-atmosphere coupling (bottom).

29. COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research 29 Contrary to the paradigm of rapid tropical error growth followed by early saturation, Tropical wind errors continue to grow even after day 30, and saturate later than extratropical errors. The predictability time is thus seen to be ‘greater’ in tropics than further poleward,especially for the planetary waves. We need to better understand the nature of tropical planetary waves beyond the MJO (the “background spectrum”) Results from an AGCM with specified SST

30. COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research 30 Normalized Error growth in u-rotational (1 – 60 days) PW: m = 1-5 SW: m = 6-20 TROPICS SH MIDLAT Planetary Waves m=1-5 Medium Waves m=6-20 200 mb top 850 mb bot

31. COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research 31 Error growth 1 – 60 days – u div PW: m = 1-5 SW: m = 6-20 Normalized Error growth in u-divergent (1 – 60 days) TROPICS SH MIDLAT Planetary Waves m=1-5 Medium Waves m=6-20 200 mb top 850 mb bot

32. COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research 32 Predictability in a Changing Climate Preliminary Results - Land-atmosphere coupling at daily time scales has the same structure as longer time sensitivites of land-atmosphere coupling - Confirmation of enhanced theoretical predictability in the tropics on a wide range of space and time scales - Little or no systematic difference seen between predictability properties based on 1850 and 2000 baseline CCSM4 runs

33. COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research 33 Intra-Seasonal to Inter-Annual Predictabilty and Prediction Conclusions (1) Uncertainty in the ocean initial conditions remain a major factor in ENSO predictability Seamless approach for Intra-seasonal to Inter-annual time scales: High resolution is critical for coherent structures (blocking, tropical cyclones) BUT Model pararmeterizations remain a stumbling block Stochastic parameterization technique to be exploited (in future work)

34. COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research 34 Intra-Seasonal to Inter-Annual Predictabilty and Prediction Conclusions (2) Basic research using “super-parameterization” and techniques for adding idealized heating has given insights into the predictability of the Indian and South American monsoons Predictability in a Changing Climate: How do fundamental predictability properties change as the climate changes? (ongoing work)